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Record W611655616

Reading aids for adults with low vision (review)

2013· article· en· W611655616 on OpenAlexaboutno aff
Gianni Virgili, Ruthy Acosta, Lori L. Grover, Sharon A Bentley, Giovanni Giacomelli

Bibliographic record

VenueDeakin Research Online (Deakin University) · 2013
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)MedicinePopulationOptometry
DOInot available

Abstract

fetched live from OpenAlex

What is the aim of this review? The aim of this Cochrane Review was to compare different reading aids for people with low vision. Cochrane Review authors collected and analysed all relevant studies to answer this question and found 13 studies. Key messages There is insufficient evidence supporting the use of a specific type of electronic or optical reading aid. The review suggests that reading speeds improve with the use of stand‐mounted electronic devices. There is little evidence for a difference between head‐mounted or portable electronic devices versus optical or other electronic devices, although technology may have improved since these studies took place. There is no evidence to support the use of filters or prism spectacles. What was studied in the review? The number of people with low vision is increasing with the ageing population. Magnifying optical and electronic aids are commonly prescribed to help people maintain the ability to read when their vision starts to fade. Cochrane authors reviewed the evidence for the effect of reading aids on reading ability in people with low vision to find out whether there are differences in reading performance using conventional optical devices, such as hand‐held or stand‐based microscopic magnifiers, as compared to electronic devices such as stand‐based, closed circuit television and hand‐held electronic magnifiers. Cochrane Review authors assessed how certain the evidence was for each review finding. They looked for factors that can make the evidence less certain, such as problems with the way the studies were done, very small studies, and inconsistent findings across studies. They also looked for factors that can make the evidence more certain, including very large effects. They graded each finding as being of very low, low, moderate or high certainty. What are the main results of the review? Cochrane Review authors found 13 relevant studies. Seven were from the USA, five from the UK and one from Canada. These studies compared the effect of different reading aids on reading performance, mainly reading speed. The participants were adults attending low vision services. Most of the people were affected by macular degeneration, which causes of loss of central vision and is often age‐related. Because most of the studies were small, the results were often imprecise, and it is difficult to know whether they apply to everyone with low vision. The results were as follows. - Reading speed may be faster with electronic devices than with optical magnifiers (moderate‐ and low‐certainty evidence). - Provision of a closed circuit television (CCTV) at an initial rehabilitation consultation may increase reading speeds compared with standard low‐vision aids prescription alone (low‐certainty evidence). - Reading speed with head‐mounted electronic devices showed inconsistent differences compared to optical devices (moderate or low‐certainty evidence). - Reading speeds with a tablet computer compared with stand‐mounted CCTV were similar (low‐certainty evidence). - Addition of an electronic portable device to a preferred optical device did not appear to increase reading speed (low‐certainty evidence). - Coloured filters were no better and possibly worse than a clear filter for reading speed (low‐certainty evidence). - Custom or standard prism spectacles did not appear to convey additional benefit compared with conventional reading spectacles for people with age‐related macular degeneration (low‐certainty evidence).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.352
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

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